import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import seaborn as sns
import csv
df = pd.read_csv(r'C:\Users\sarahd\Desktop\light_duty_vehicles.csv',low_memory=False)
df
| Unnamed: 0 | Vehicle_ID | Model | Model_Year | Alternative_Fuel_Economy_City | Alternative_Fuel_Economy_Highway | Alternative_Fuel_Economy_Combined | Conventional_Fuel_Economy_City | Conventional_Fuel_Economy_Highway | Conventional_Fuel_Economy_Combined | ... | Engine_Description | Manufacturer | Category | Fuel | Electric-Only_Range | PHEV_Total_Range | PHEV_Type | Drivetrain | combined_fuel_economy | Name | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | 13044.0 | NSX | 2022.0 | NaN | NaN | NaN | 21.0 | 22.0 | 21.0 | ... | 3.5L V6 | Acura | Sedan/Wagon | Hybrid Electric | NaN | NaN | NaN | AWD | NaN | Acura NSX |
| 1 | 1 | 12854.0 | A3 | 2022.0 | NaN | NaN | NaN | 29.0 | 38.0 | 32.0 | ... | 2.0L I4 | Audi | Sedan/Wagon | Hybrid Electric | NaN | NaN | NaN | FWD | NaN | Audi A3 |
| 2 | 2 | 12842.0 | A3 quattro | 2022.0 | NaN | NaN | NaN | 28.0 | 36.0 | 31.0 | ... | 2.0L I4 | Audi | Sedan/Wagon | Hybrid Electric | NaN | NaN | NaN | AWD | NaN | Audi A3 quattro |
| 3 | 3 | 12783.0 | A4 allroad quattro | 2022.0 | NaN | NaN | NaN | 24.0 | 30.0 | 26.0 | ... | 2.0L I4 | Audi | Sedan/Wagon | Hybrid Electric | NaN | NaN | NaN | AWD | NaN | Audi A4 allroad quattro |
| 4 | 4 | 12782.0 | A4 quattro | 2022.0 | NaN | NaN | NaN | 26.0 | 34.0 | 29.0 | ... | 2.0L I4 | Audi | Sedan/Wagon | Hybrid Electric | NaN | NaN | NaN | AWD | NaN | Audi A4 quattro |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 1048570 | 1048570 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 1048571 | 1048571 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 1048572 | 1048572 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 1048573 | 1048573 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 1048574 | 1048574 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 66.0 | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
1048575 rows × 24 columns
df.head()
| Unnamed: 0 | Vehicle_ID | Model | Model_Year | Alternative_Fuel_Economy_City | Alternative_Fuel_Economy_Highway | Alternative_Fuel_Economy_Combined | Conventional_Fuel_Economy_City | Conventional_Fuel_Economy_Highway | Conventional_Fuel_Economy_Combined | ... | Engine_Description | Manufacturer | Category | Fuel | Electric-Only_Range | PHEV_Total_Range | PHEV_Type | Drivetrain | combined_fuel_economy | Name | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | 13044.0 | NSX | 2022.0 | NaN | NaN | NaN | 21.0 | 22.0 | 21.0 | ... | 3.5L V6 | Acura | Sedan/Wagon | Hybrid Electric | NaN | NaN | NaN | AWD | NaN | Acura NSX |
| 1 | 1 | 12854.0 | A3 | 2022.0 | NaN | NaN | NaN | 29.0 | 38.0 | 32.0 | ... | 2.0L I4 | Audi | Sedan/Wagon | Hybrid Electric | NaN | NaN | NaN | FWD | NaN | Audi A3 |
| 2 | 2 | 12842.0 | A3 quattro | 2022.0 | NaN | NaN | NaN | 28.0 | 36.0 | 31.0 | ... | 2.0L I4 | Audi | Sedan/Wagon | Hybrid Electric | NaN | NaN | NaN | AWD | NaN | Audi A3 quattro |
| 3 | 3 | 12783.0 | A4 allroad quattro | 2022.0 | NaN | NaN | NaN | 24.0 | 30.0 | 26.0 | ... | 2.0L I4 | Audi | Sedan/Wagon | Hybrid Electric | NaN | NaN | NaN | AWD | NaN | Audi A4 allroad quattro |
| 4 | 4 | 12782.0 | A4 quattro | 2022.0 | NaN | NaN | NaN | 26.0 | 34.0 | 29.0 | ... | 2.0L I4 | Audi | Sedan/Wagon | Hybrid Electric | NaN | NaN | NaN | AWD | NaN | Audi A4 quattro |
5 rows × 24 columns
df.describe()
| Unnamed: 0 | Vehicle_ID | Model_Year | Alternative_Fuel_Economy_City | Alternative_Fuel_Economy_Highway | Alternative_Fuel_Economy_Combined | Conventional_Fuel_Economy_City | Conventional_Fuel_Economy_Highway | Conventional_Fuel_Economy_Combined | Engine_Cylinder_Count | Electric-Only_Range | PHEV_Total_Range | combined_fuel_economy | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 1.048575e+06 | 3008.00000 | 3008.000000 | 1595.000000 | 1505.000000 | 467.000000 | 2044.000000 | 2047.000000 | 763.000000 | 2443.000000 | 374.000000 | 43.000000 | 253.000000 |
| mean | 5.242870e+05 | 9424.59242 | 2014.493019 | 38.439298 | 37.392651 | 72.126338 | 22.781605 | 27.360430 | 27.832241 | 6.085960 | 148.251337 | 450.465116 | 75.418972 |
| std | 3.026977e+05 | 4667.98027 | 6.722191 | 47.778798 | 42.724296 | 36.776309 | 10.794132 | 9.105821 | 10.265666 | 1.762977 | 128.708353 | 93.042578 | 41.141335 |
| min | 0.000000e+00 | 1.00000 | 1991.000000 | 0.000000 | 0.000000 | 10.000000 | 0.000000 | 0.000000 | 15.000000 | 0.000000 | 8.000000 | 290.000000 | 25.000000 |
| 25% | 2.621435e+05 | 10280.75000 | 2012.000000 | 11.000000 | 16.000000 | 49.000000 | 16.000000 | 21.000000 | 21.000000 | 4.000000 | 21.000000 | 380.000000 | 34.000000 |
| 50% | 5.242870e+05 | 11560.50000 | 2016.000000 | 14.000000 | 20.000000 | 74.000000 | 19.000000 | 26.000000 | 24.000000 | 6.000000 | 126.000000 | 460.000000 | 74.000000 |
| 75% | 7.864305e+05 | 12336.25000 | 2020.000000 | 60.000000 | 41.000000 | 103.500000 | 26.000000 | 31.000000 | 31.000000 | 8.000000 | 254.750000 | 520.000000 | 99.000000 |
| max | 1.048574e+06 | 13105.00000 | 2022.000000 | 800.000000 | 800.000000 | 142.000000 | 66.000000 | 66.000000 | 59.000000 | 12.000000 | 520.000000 | 640.000000 | 187.000000 |
df.nunique()
Unnamed: 0 1048575 Vehicle_ID 3008 Model 1286 Model_Year 32 Alternative_Fuel_Economy_City 138 Alternative_Fuel_Economy_Highway 116 Alternative_Fuel_Economy_Combined 96 Conventional_Fuel_Economy_City 54 Conventional_Fuel_Economy_Highway 51 Conventional_Fuel_Economy_Combined 41 Transmission_Type 64 Engine_Type 115 Engine_Size 255 Engine_Cylinder_Count 8 Engine_Description 356 Manufacturer 55 Category 6 Fuel 11 Electric-Only_Range 144 PHEV_Total_Range 25 PHEV_Type 2 Drivetrain 5 combined_fuel_economy 72 Name 1292 dtype: int64
df.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 1048575 entries, 0 to 1048574 Data columns (total 24 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Unnamed: 0 1048575 non-null int64 1 Vehicle_ID 3008 non-null float64 2 Model 3008 non-null object 3 Model_Year 3008 non-null float64 4 Alternative_Fuel_Economy_City 1595 non-null float64 5 Alternative_Fuel_Economy_Highway 1505 non-null float64 6 Alternative_Fuel_Economy_Combined 467 non-null float64 7 Conventional_Fuel_Economy_City 2044 non-null float64 8 Conventional_Fuel_Economy_Highway 2047 non-null float64 9 Conventional_Fuel_Economy_Combined 763 non-null float64 10 Transmission_Type 2924 non-null object 11 Engine_Type 2211 non-null object 12 Engine_Size 2874 non-null object 13 Engine_Cylinder_Count 2443 non-null float64 14 Engine_Description 1971 non-null object 15 Manufacturer 3008 non-null object 16 Category 3008 non-null object 17 Fuel 3008 non-null object 18 Electric-Only_Range 374 non-null float64 19 PHEV_Total_Range 43 non-null float64 20 PHEV_Type 176 non-null object 21 Drivetrain 329 non-null object 22 combined_fuel_economy 253 non-null float64 23 Name 3008 non-null object dtypes: float64(12), int64(1), object(11) memory usage: 192.0+ MB
df.columns.tolist()
['Unnamed: 0', 'Vehicle_ID', 'Model', 'Model_Year', 'Alternative_Fuel_Economy_City', 'Alternative_Fuel_Economy_Highway', 'Alternative_Fuel_Economy_Combined', 'Conventional_Fuel_Economy_City', 'Conventional_Fuel_Economy_Highway', 'Conventional_Fuel_Economy_Combined', 'Transmission_Type', 'Engine_Type', 'Engine_Size', 'Engine_Cylinder_Count', 'Engine_Description', 'Manufacturer', 'Category', 'Fuel', 'Electric-Only_Range', 'PHEV_Total_Range', 'PHEV_Type', 'Drivetrain', 'combined_fuel_economy', 'Name']
fuels=df['Fuel'].unique().tolist()
fuels
['Hybrid Electric', 'Plug-in Hybrid Electric', 'Electric', 'Biodiesel (B20)', 'Ethanol (E85)', 'Hydrogen Fuel Cell', 'Propane - Bi-fuel', 'CNG - Bi-fuel', 'CNG - Compressed Natural Gas', 'Propane', 'Methanol', nan]
man=df['Manufacturer'].unique().tolist()
man
['Acura', 'Audi', 'Bentley Motors', 'BMW', 'Cadillac', 'Chevrolet', 'Chrysler', 'Ferrari', 'Ford', 'GMC', 'Honda', 'Hyundai', 'Jaguar', 'Jeep', 'Kia', 'Land Rover', 'Lexus', 'Lincoln', 'Lucid USA, Inc.', 'Mazda', 'Mercedes-Benz', 'Mini', 'Mitsubishi', 'Nissan', 'Polestar Automotive USA', 'Porsche', 'Ram', 'Rivian ', 'Tesla', 'Toyota', 'Volkswagen', 'Volvo', 'Kandi', 'Karma', 'Subaru', 'Buick', 'BYD Motors', 'Dodge', 'Fiat', 'smart', 'Infiniti', 'McLaren', 'Scion', 'Coda Automotive', 'Vehicle Production Group', 'Fisker Automotive', 'Wheego Electric Cars, Inc.', 'Mercury', 'Saab', 'HUMMER', 'Saturn', 'Solectria', 'QUANTUM-PROCON', 'General Motors EV', 'Plymouth', nan]
df2 = df[["Manufacturer","Fuel","Category","Drivetrain"]]
df2
| Manufacturer | Fuel | Category | Drivetrain | |
|---|---|---|---|---|
| 0 | Acura | Hybrid Electric | Sedan/Wagon | AWD |
| 1 | Audi | Hybrid Electric | Sedan/Wagon | FWD |
| 2 | Audi | Hybrid Electric | Sedan/Wagon | AWD |
| 3 | Audi | Hybrid Electric | Sedan/Wagon | AWD |
| 4 | Audi | Hybrid Electric | Sedan/Wagon | AWD |
| ... | ... | ... | ... | ... |
| 1048570 | NaN | NaN | NaN | NaN |
| 1048571 | NaN | NaN | NaN | NaN |
| 1048572 | NaN | NaN | NaN | NaN |
| 1048573 | NaN | NaN | NaN | NaN |
| 1048574 | NaN | NaN | NaN | NaN |
1048575 rows × 4 columns
df_hybrid=df.query("Fuel == 'Hybrid Electric'")
df_hybrid
| Unnamed: 0 | Vehicle_ID | Model | Model_Year | Alternative_Fuel_Economy_City | Alternative_Fuel_Economy_Highway | Alternative_Fuel_Economy_Combined | Conventional_Fuel_Economy_City | Conventional_Fuel_Economy_Highway | Conventional_Fuel_Economy_Combined | ... | Engine_Description | Manufacturer | Category | Fuel | Electric-Only_Range | PHEV_Total_Range | PHEV_Type | Drivetrain | combined_fuel_economy | Name | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | 13044.0 | NSX | 2022.0 | NaN | NaN | NaN | 21.0 | 22.0 | 21.0 | ... | 3.5L V6 | Acura | Sedan/Wagon | Hybrid Electric | NaN | NaN | NaN | AWD | NaN | Acura NSX |
| 1 | 1 | 12854.0 | A3 | 2022.0 | NaN | NaN | NaN | 29.0 | 38.0 | 32.0 | ... | 2.0L I4 | Audi | Sedan/Wagon | Hybrid Electric | NaN | NaN | NaN | FWD | NaN | Audi A3 |
| 2 | 2 | 12842.0 | A3 quattro | 2022.0 | NaN | NaN | NaN | 28.0 | 36.0 | 31.0 | ... | 2.0L I4 | Audi | Sedan/Wagon | Hybrid Electric | NaN | NaN | NaN | AWD | NaN | Audi A3 quattro |
| 3 | 3 | 12783.0 | A4 allroad quattro | 2022.0 | NaN | NaN | NaN | 24.0 | 30.0 | 26.0 | ... | 2.0L I4 | Audi | Sedan/Wagon | Hybrid Electric | NaN | NaN | NaN | AWD | NaN | Audi A4 allroad quattro |
| 4 | 4 | 12782.0 | A4 quattro | 2022.0 | NaN | NaN | NaN | 26.0 | 34.0 | 29.0 | ... | 2.0L I4 | Audi | Sedan/Wagon | Hybrid Electric | NaN | NaN | NaN | AWD | NaN | Audi A4 quattro |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 2794 | 2794 | 545.0 | Prius Hybrid | 2002.0 | 60.0 | 51.0 | NaN | NaN | NaN | NaN | ... | NaN | Toyota | Sedan/Wagon | Hybrid Electric | NaN | NaN | NaN | NaN | NaN | Toyota Prius Hybrid |
| 2829 | 2829 | 139.0 | Insight | 2001.0 | 61.0 | 70.0 | NaN | NaN | NaN | NaN | ... | NaN | Honda | Sedan/Wagon | Hybrid Electric | NaN | NaN | NaN | NaN | NaN | Honda Insight |
| 2838 | 2838 | 140.0 | Prius Hybrid | 2001.0 | 52.0 | 45.0 | NaN | NaN | NaN | NaN | ... | NaN | Toyota | Sedan/Wagon | Hybrid Electric | NaN | NaN | NaN | NaN | NaN | Toyota Prius Hybrid |
| 2870 | 2870 | 80.0 | Insight | 2000.0 | 61.0 | 70.0 | NaN | 20.0 | 26.0 | NaN | ... | NaN | Honda | Sedan/Wagon | Hybrid Electric | NaN | NaN | NaN | NaN | NaN | Honda Insight |
| 2877 | 2877 | 574.0 | Prius | 2000.0 | 52.0 | 45.0 | NaN | NaN | NaN | NaN | ... | NaN | Toyota | Sedan/Wagon | Hybrid Electric | NaN | NaN | NaN | NaN | NaN | Toyota Prius |
810 rows × 24 columns
df_phev=df.query("Fuel == 'Plug-in Hybrid Electric'")
df_phev
| Unnamed: 0 | Vehicle_ID | Model | Model_Year | Alternative_Fuel_Economy_City | Alternative_Fuel_Economy_Highway | Alternative_Fuel_Economy_Combined | Conventional_Fuel_Economy_City | Conventional_Fuel_Economy_Highway | Conventional_Fuel_Economy_Combined | ... | Engine_Description | Manufacturer | Category | Fuel | Electric-Only_Range | PHEV_Total_Range | PHEV_Type | Drivetrain | combined_fuel_economy | Name | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 14 | 14 | 12831.0 | A7 TFSI e quattro | 2022.0 | NaN | NaN | 70.0 | 25.0 | 31.0 | 27.0 | ... | 2.0L I4;\r\n105 kW electric motor | Audi | Sedan/Wagon | Plug-in Hybrid Electric | 26.0 | 410.0 | Blended | AWD | 97.0 | Audi A7 TFSI e quattro |
| 28 | 28 | 12832.0 | Q5 TFSI e quattro | 2022.0 | NaN | NaN | 61.0 | 25.0 | 27.0 | 26.0 | ... | 2.0L I4;\r\n105 kW electric motor | Audi | Sedan/Wagon | Plug-in Hybrid Electric | 23.0 | 390.0 | Blended | AWD | 87.0 | Audi Q5 TFSI e quattro |
| 38 | 38 | 13090.0 | Flying Spur Hybrid | 2022.0 | NaN | NaN | 46.0 | 17.0 | 22.0 | 19.0 | ... | 2.9L V6;\r\n103 kW electric motor | Bentley Motors | Sedan/Wagon | Plug-in Hybrid Electric | 21.0 | 430.0 | Blended | NaN | 65.0 | Bentley Motors Flying Spur Hybrid |
| 39 | 39 | 13067.0 | 330e Sedan | 2022.0 | NaN | NaN | 75.0 | 25.0 | 33.0 | 28.0 | ... | 2.0L I4;\r\n80 kW electric motor | BMW | Sedan/Wagon | Plug-in Hybrid Electric | 23.0 | 320.0 | Blended | RWD | 103.0 | BMW 330e Sedan |
| 40 | 40 | 13068.0 | 330e xDrive | 2022.0 | NaN | NaN | 67.0 | 22.0 | 30.0 | 25.0 | ... | 2.0L I4;\r\n80 kW electric motor | BMW | Sedan/Wagon | Plug-in Hybrid Electric | 20.0 | 290.0 | Blended | AWD | 92.0 | BMW 330e xDrive |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 2127 | 2127 | 10553.0 | Prius Plug-in | 2013.0 | NaN | NaN | NaN | 51.0 | 49.0 | NaN | ... | NaN | Toyota | Sedan/Wagon | Plug-in Hybrid Electric | NaN | NaN | NaN | NaN | NaN | Toyota Prius Plug-in |
| 2167 | 2167 | 10351.0 | Volt | 2012.0 | 95.00 | 93.00 | NaN | 35.0 | 40.0 | NaN | ... | NaN | Chevrolet | Sedan/Wagon | Plug-in Hybrid Electric | NaN | NaN | NaN | NaN | NaN | Chevrolet Volt |
| 2179 | 2179 | 10660.0 | Karma | 2012.0 | 62.00 | 63.00 | NaN | 20.0 | 21.0 | NaN | ... | NaN | Fisker Automotive | Sedan/Wagon | Plug-in Hybrid Electric | NaN | NaN | NaN | NaN | NaN | Fisker Automotive Karma |
| 2250 | 2250 | 10385.0 | Prius Plug-in Hybrid | 2012.0 | 95.00 | NaN | NaN | 51.0 | 49.0 | NaN | ... | NaN | Toyota | Sedan/Wagon | Plug-in Hybrid Electric | NaN | NaN | NaN | NaN | NaN | Toyota Prius Plug-in Hybrid |
| 2290 | 2290 | 10126.0 | Volt | 2011.0 | 0.36 | 0.37 | NaN | 35.0 | 40.0 | NaN | ... | NaN | Chevrolet | Sedan/Wagon | Plug-in Hybrid Electric | NaN | NaN | NaN | NaN | NaN | Chevrolet Volt |
282 rows × 24 columns
display(df['Manufacturer'].value_counts())
Ford 533 Chevrolet 371 GMC 252 Toyota 193 Mercedes-Benz 146 Dodge 130 Audi 118 BMW 110 Honda 99 Tesla 93 Lexus 87 Porsche 76 Nissan 76 Hyundai 74 Chrysler 63 Ram 57 Kia 53 Land Rover 49 Jeep 38 Jaguar 34 Volvo 33 Cadillac 33 Lincoln 28 Volkswagen 25 Buick 23 Mercury 23 Infiniti 22 Bentley Motors 20 Acura 18 Mazda 14 Mitsubishi 12 Solectria 12 smart 10 General Motors EV 9 Mini 8 Fiat 7 Subaru 6 Lucid USA, Inc. 6 Karma 5 Polestar Automotive USA 5 Saturn 5 QUANTUM-PROCON 4 Plymouth 4 Ferrari 4 Vehicle Production Group 3 McLaren 3 BYD Motors 3 Coda Automotive 2 Scion 2 Rivian 2 Fisker Automotive 1 Wheego Electric Cars, Inc. 1 Saab 1 HUMMER 1 Kandi 1 Name: Manufacturer, dtype: int64
#because ford has the most counts from above
df2=df[['Manufacturer','Fuel']]
df2['count']=1
df3=df2.groupby(['Manufacturer','Fuel'],as_index=False).sum()
display(df3)
C:\Users\sarahd\AppData\Local\Temp\ipykernel_19892\1095849869.py:3: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy df2['count']=1
| Manufacturer | Fuel | count | |
|---|---|---|---|
| 0 | Acura | Hybrid Electric | 18 |
| 1 | Audi | Electric | 14 |
| 2 | Audi | Ethanol (E85) | 23 |
| 3 | Audi | Hybrid Electric | 70 |
| 4 | Audi | Plug-in Hybrid Electric | 11 |
| ... | ... | ... | ... |
| 143 | Volkswagen | Hybrid Electric | 9 |
| 144 | Volvo | Electric | 3 |
| 145 | Volvo | Plug-in Hybrid Electric | 30 |
| 146 | Wheego Electric Cars, Inc. | Electric | 1 |
| 147 | smart | Electric | 10 |
148 rows × 3 columns
for man in man:
df_ford=df3[df3['Manufacturer']==man].sort_values('count',ascending=False)
display(df_ford)
| Manufacturer | Fuel | count | |
|---|---|---|---|
| 0 | Acura | Hybrid Electric | 18 |
| Manufacturer | Fuel | count | |
|---|---|---|---|
| 3 | Audi | Hybrid Electric | 70 |
| 2 | Audi | Ethanol (E85) | 23 |
| 1 | Audi | Electric | 14 |
| 4 | Audi | Plug-in Hybrid Electric | 11 |
| Manufacturer | Fuel | count | |
|---|---|---|---|
| 9 | Bentley Motors | Ethanol (E85) | 17 |
| 10 | Bentley Motors | Plug-in Hybrid Electric | 3 |
| Manufacturer | Fuel | count | |
|---|---|---|---|
| 7 | BMW | Plug-in Hybrid Electric | 49 |
| 6 | BMW | Hybrid Electric | 41 |
| 5 | BMW | Electric | 20 |
| Manufacturer | Fuel | count | |
|---|---|---|---|
| 14 | Cadillac | Ethanol (E85) | 19 |
| 15 | Cadillac | Hybrid Electric | 5 |
| 16 | Cadillac | Plug-in Hybrid Electric | 5 |
| 13 | Cadillac | Biodiesel (B20) | 4 |
| Manufacturer | Fuel | count | |
|---|---|---|---|
| 21 | Chevrolet | Ethanol (E85) | 191 |
| 17 | Chevrolet | Biodiesel (B20) | 64 |
| 19 | Chevrolet | CNG - Compressed Natural Gas | 45 |
| 22 | Chevrolet | Hybrid Electric | 25 |
| 20 | Chevrolet | Electric | 17 |
| 25 | Chevrolet | Propane | 11 |
| 24 | Chevrolet | Plug-in Hybrid Electric | 9 |
| 18 | Chevrolet | CNG - Bi-fuel | 6 |
| 23 | Chevrolet | Methanol | 2 |
| 26 | Chevrolet | Propane - Bi-fuel | 1 |
| Manufacturer | Fuel | count | |
|---|---|---|---|
| 28 | Chrysler | Ethanol (E85) | 54 |
| 30 | Chrysler | Plug-in Hybrid Electric | 6 |
| 27 | Chrysler | Electric | 2 |
| 29 | Chrysler | Hybrid Electric | 1 |
| Manufacturer | Fuel | count | |
|---|---|---|---|
| 38 | Ferrari | Plug-in Hybrid Electric | 3 |
| 37 | Ferrari | Hybrid Electric | 1 |
| Manufacturer | Fuel | count | |
|---|---|---|---|
| 45 | Ford | Ethanol (E85) | 188 |
| 43 | Ford | CNG - Compressed Natural Gas | 94 |
| 46 | Ford | Hybrid Electric | 52 |
| 49 | Ford | Propane | 48 |
| 44 | Ford | Electric | 43 |
| 41 | Ford | Biodiesel (B20) | 32 |
| 42 | Ford | CNG - Bi-fuel | 23 |
| 48 | Ford | Plug-in Hybrid Electric | 23 |
| 50 | Ford | Propane - Bi-fuel | 22 |
| 47 | Ford | Methanol | 8 |
| Manufacturer | Fuel | count | |
|---|---|---|---|
| 54 | GMC | Ethanol (E85) | 128 |
| 51 | GMC | Biodiesel (B20) | 53 |
| 53 | GMC | CNG - Compressed Natural Gas | 36 |
| 55 | GMC | Hybrid Electric | 18 |
| 56 | GMC | Propane | 12 |
| 52 | GMC | CNG - Bi-fuel | 4 |
| 57 | GMC | Propane - Bi-fuel | 1 |
| Manufacturer | Fuel | count | |
|---|---|---|---|
| 62 | Honda | Hybrid Electric | 57 |
| 60 | Honda | CNG - Compressed Natural Gas | 17 |
| 63 | Honda | Hydrogen Fuel Cell | 11 |
| 61 | Honda | Electric | 8 |
| 64 | Honda | Plug-in Hybrid Electric | 6 |
| Manufacturer | Fuel | count | |
|---|---|---|---|
| 66 | Hyundai | Hybrid Electric | 40 |
| 65 | Hyundai | Electric | 12 |
| 67 | Hyundai | Hydrogen Fuel Cell | 11 |
| 68 | Hyundai | Plug-in Hybrid Electric | 11 |
| Manufacturer | Fuel | count | |
|---|---|---|---|
| 70 | Jaguar | Biodiesel (B20) | 14 |
| 72 | Jaguar | Ethanol (E85) | 11 |
| 73 | Jaguar | Hybrid Electric | 6 |
| 71 | Jaguar | Electric | 3 |
| Manufacturer | Fuel | count | |
|---|---|---|---|
| 75 | Jeep | Ethanol (E85) | 21 |
| 76 | Jeep | Hybrid Electric | 11 |
| 74 | Jeep | Biodiesel (B20) | 3 |
| 77 | Jeep | Plug-in Hybrid Electric | 3 |
| Manufacturer | Fuel | count | |
|---|---|---|---|
| 81 | Kia | Hybrid Electric | 31 |
| 80 | Kia | Electric | 12 |
| 82 | Kia | Plug-in Hybrid Electric | 10 |
| Manufacturer | Fuel | count | |
|---|---|---|---|
| 85 | Land Rover | Hybrid Electric | 26 |
| 83 | Land Rover | Biodiesel (B20) | 11 |
| 84 | Land Rover | Ethanol (E85) | 7 |
| 86 | Land Rover | Plug-in Hybrid Electric | 5 |
| Manufacturer | Fuel | count | |
|---|---|---|---|
| 87 | Lexus | Hybrid Electric | 86 |
| 88 | Lexus | Plug-in Hybrid Electric | 1 |
| Manufacturer | Fuel | count | |
|---|---|---|---|
| 89 | Lincoln | Ethanol (E85) | 13 |
| 90 | Lincoln | Hybrid Electric | 10 |
| 91 | Lincoln | Plug-in Hybrid Electric | 5 |
| Manufacturer | Fuel | count | |
|---|---|---|---|
| 92 | Lucid USA, Inc. | Electric | 6 |
| Manufacturer | Fuel | count | |
|---|---|---|---|
| 94 | Mazda | Ethanol (E85) | 9 |
| 95 | Mazda | Hybrid Electric | 4 |
| 93 | Mazda | Electric | 1 |
| Manufacturer | Fuel | count | |
|---|---|---|---|
| 99 | Mercedes-Benz | Hybrid Electric | 82 |
| 98 | Mercedes-Benz | Ethanol (E85) | 42 |
| 101 | Mercedes-Benz | Plug-in Hybrid Electric | 13 |
| 97 | Mercedes-Benz | Electric | 6 |
| 100 | Mercedes-Benz | Hydrogen Fuel Cell | 3 |
| Manufacturer | Fuel | count | |
|---|---|---|---|
| 105 | Mini | Plug-in Hybrid Electric | 5 |
| 104 | Mini | Electric | 3 |
| Manufacturer | Fuel | count | |
|---|---|---|---|
| 106 | Mitsubishi | Electric | 6 |
| 108 | Mitsubishi | Plug-in Hybrid Electric | 5 |
| 107 | Mitsubishi | Ethanol (E85) | 1 |
| Manufacturer | Fuel | count | |
|---|---|---|---|
| 110 | Nissan | Electric | 29 |
| 111 | Nissan | Ethanol (E85) | 28 |
| 112 | Nissan | Hybrid Electric | 16 |
| 109 | Nissan | Biodiesel (B20) | 3 |
| Manufacturer | Fuel | count | |
|---|---|---|---|
| 115 | Polestar Automotive USA | Electric | 3 |
| 116 | Polestar Automotive USA | Plug-in Hybrid Electric | 2 |
| Manufacturer | Fuel | count | |
|---|---|---|---|
| 119 | Porsche | Plug-in Hybrid Electric | 52 |
| 117 | Porsche | Electric | 19 |
| 118 | Porsche | Hybrid Electric | 5 |
| Manufacturer | Fuel | count | |
|---|---|---|---|
| 125 | Ram | Hybrid Electric | 20 |
| 121 | Ram | Biodiesel (B20) | 16 |
| 124 | Ram | Ethanol (E85) | 15 |
| 122 | Ram | CNG - Bi-fuel | 3 |
| 123 | Ram | CNG - Compressed Natural Gas | 3 |
| Manufacturer | Fuel | count | |
|---|---|---|---|
| 126 | Rivian | Electric | 2 |
| Manufacturer | Fuel | count | |
|---|---|---|---|
| 133 | Tesla | Electric | 93 |
| Manufacturer | Fuel | count | |
|---|---|---|---|
| 137 | Toyota | Hybrid Electric | 133 |
| 136 | Toyota | Ethanol (E85) | 21 |
| 139 | Toyota | Plug-in Hybrid Electric | 13 |
| 135 | Toyota | Electric | 12 |
| 138 | Toyota | Hydrogen Fuel Cell | 11 |
| 134 | Toyota | CNG - Compressed Natural Gas | 3 |
| Manufacturer | Fuel | count | |
|---|---|---|---|
| 141 | Volkswagen | Electric | 14 |
| 143 | Volkswagen | Hybrid Electric | 9 |
| 142 | Volkswagen | Ethanol (E85) | 2 |
| Manufacturer | Fuel | count | |
|---|---|---|---|
| 145 | Volvo | Plug-in Hybrid Electric | 30 |
| 144 | Volvo | Electric | 3 |
| Manufacturer | Fuel | count | |
|---|---|---|---|
| 78 | Kandi | Electric | 1 |
| Manufacturer | Fuel | count | |
|---|---|---|---|
| 79 | Karma | Plug-in Hybrid Electric | 5 |
| Manufacturer | Fuel | count | |
|---|---|---|---|
| 131 | Subaru | Hybrid Electric | 3 |
| 132 | Subaru | Plug-in Hybrid Electric | 3 |
| Manufacturer | Fuel | count | |
|---|---|---|---|
| 11 | Buick | Ethanol (E85) | 19 |
| 12 | Buick | Hybrid Electric | 4 |
| Manufacturer | Fuel | count | |
|---|---|---|---|
| 8 | BYD Motors | Electric | 3 |
| Manufacturer | Fuel | count | |
|---|---|---|---|
| 34 | Dodge | Ethanol (E85) | 94 |
| 32 | Dodge | CNG - Compressed Natural Gas | 28 |
| 36 | Dodge | Methanol | 4 |
| 33 | Dodge | Electric | 3 |
| 35 | Dodge | Hybrid Electric | 1 |
| Manufacturer | Fuel | count | |
|---|---|---|---|
| 39 | Fiat | Electric | 7 |
| Manufacturer | Fuel | count | |
|---|---|---|---|
| 147 | smart | Electric | 10 |
| Manufacturer | Fuel | count | |
|---|---|---|---|
| 69 | Infiniti | Hybrid Electric | 22 |
| Manufacturer | Fuel | count | |
|---|---|---|---|
| 96 | McLaren | Plug-in Hybrid Electric | 3 |
| Manufacturer | Fuel | count | |
|---|---|---|---|
| 129 | Scion | Electric | 2 |
| Manufacturer | Fuel | count | |
|---|---|---|---|
| 31 | Coda Automotive | Electric | 2 |
| Manufacturer | Fuel | count | |
|---|---|---|---|
| 140 | Vehicle Production Group | CNG - Compressed Natural Gas | 3 |
| Manufacturer | Fuel | count | |
|---|---|---|---|
| 40 | Fisker Automotive | Plug-in Hybrid Electric | 1 |
| Manufacturer | Fuel | count | |
|---|---|---|---|
| 146 | Wheego Electric Cars, Inc. | Electric | 1 |
| Manufacturer | Fuel | count | |
|---|---|---|---|
| 102 | Mercury | Ethanol (E85) | 15 |
| 103 | Mercury | Hybrid Electric | 8 |
| Manufacturer | Fuel | count | |
|---|---|---|---|
| 127 | Saab | Ethanol (E85) | 1 |
| Manufacturer | Fuel | count | |
|---|---|---|---|
| 59 | HUMMER | Ethanol (E85) | 1 |
| Manufacturer | Fuel | count | |
|---|---|---|---|
| 128 | Saturn | Hybrid Electric | 5 |
| Manufacturer | Fuel | count | |
|---|---|---|---|
| 130 | Solectria | Electric | 12 |
| Manufacturer | Fuel | count | |
|---|---|---|---|
| 120 | QUANTUM-PROCON | Propane | 4 |
| Manufacturer | Fuel | count | |
|---|---|---|---|
| 58 | General Motors EV | Electric | 9 |
| Manufacturer | Fuel | count | |
|---|---|---|---|
| 113 | Plymouth | CNG - Compressed Natural Gas | 2 |
| 114 | Plymouth | Ethanol (E85) | 2 |
| Manufacturer | Fuel | count |
|---|
df['combined_fuel_economy'] = df["Alternative_Fuel_Economy_Combined"] + df["Conventional_Fuel_Economy_Combined"]
print('Updated DataFrame:')
print(df)
Updated DataFrame:
Unnamed: 0 Vehicle_ID Model Model_Year \
0 0 13044.0 NSX 2022.0
1 1 12854.0 A3 2022.0
2 2 12842.0 A3 quattro 2022.0
3 3 12783.0 A4 allroad quattro 2022.0
4 4 12782.0 A4 quattro 2022.0
... ... ... ... ...
1048570 1048570 NaN NaN NaN
1048571 1048571 NaN NaN NaN
1048572 1048572 NaN NaN NaN
1048573 1048573 NaN NaN NaN
1048574 1048574 NaN NaN NaN
Alternative_Fuel_Economy_City Alternative_Fuel_Economy_Highway \
0 NaN NaN
1 NaN NaN
2 NaN NaN
3 NaN NaN
4 NaN NaN
... ... ...
1048570 NaN NaN
1048571 NaN NaN
1048572 NaN NaN
1048573 NaN NaN
1048574 NaN NaN
Alternative_Fuel_Economy_Combined Conventional_Fuel_Economy_City \
0 NaN 21.0
1 NaN 29.0
2 NaN 28.0
3 NaN 24.0
4 NaN 26.0
... ... ...
1048570 NaN NaN
1048571 NaN NaN
1048572 NaN NaN
1048573 NaN NaN
1048574 NaN NaN
Conventional_Fuel_Economy_Highway \
0 22.0
1 38.0
2 36.0
3 30.0
4 34.0
... ...
1048570 NaN
1048571 NaN
1048572 NaN
1048573 NaN
1048574 66.0
Conventional_Fuel_Economy_Combined ... Engine_Description \
0 21.0 ... 3.5L V6
1 32.0 ... 2.0L I4
2 31.0 ... 2.0L I4
3 26.0 ... 2.0L I4
4 29.0 ... 2.0L I4
... ... ... ...
1048570 NaN ... NaN
1048571 NaN ... NaN
1048572 NaN ... NaN
1048573 NaN ... NaN
1048574 NaN ... NaN
Manufacturer Category Fuel Electric-Only_Range \
0 Acura Sedan/Wagon Hybrid Electric NaN
1 Audi Sedan/Wagon Hybrid Electric NaN
2 Audi Sedan/Wagon Hybrid Electric NaN
3 Audi Sedan/Wagon Hybrid Electric NaN
4 Audi Sedan/Wagon Hybrid Electric NaN
... ... ... ... ...
1048570 NaN NaN NaN NaN
1048571 NaN NaN NaN NaN
1048572 NaN NaN NaN NaN
1048573 NaN NaN NaN NaN
1048574 NaN NaN NaN NaN
PHEV_Total_Range PHEV_Type Drivetrain combined_fuel_economy \
0 NaN NaN AWD NaN
1 NaN NaN FWD NaN
2 NaN NaN AWD NaN
3 NaN NaN AWD NaN
4 NaN NaN AWD NaN
... ... ... ... ...
1048570 NaN NaN NaN NaN
1048571 NaN NaN NaN NaN
1048572 NaN NaN NaN NaN
1048573 NaN NaN NaN NaN
1048574 NaN NaN NaN NaN
Name
0 Acura NSX
1 Audi A3
2 Audi A3 quattro
3 Audi A4 allroad quattro
4 Audi A4 quattro
... ...
1048570 NaN
1048571 NaN
1048572 NaN
1048573 NaN
1048574 NaN
[1048575 rows x 24 columns]
df['Name']=df['Manufacturer']+' '+df['Model']
df_fe=df[df['Fuel']=='Electric'].reset_index(drop=True)
display(df_fe[0:2].T)
df_fe_aer=df_fe[['Name', 'Electric-Only_Range']]
df_fe_aer=df_fe_aer.dropna().drop_duplicates()
df_fe_aer=df_fe_aer.groupby('Name',as_index=False).max()
df_fe_aer=df_fe_aer.sort_values('Electric-Only_Range',ascending=False)
| 0 | 1 | |
|---|---|---|
| Unnamed: 0 | 16 | 17 |
| Vehicle_ID | 12949.0 | 13002.0 |
| Model | e-tron GT | e-tron quattro |
| Model_Year | 2022.0 | 2022.0 |
| Alternative_Fuel_Economy_City | 81.0 | 78.0 |
| Alternative_Fuel_Economy_Highway | 83.0 | 77.0 |
| Alternative_Fuel_Economy_Combined | 82.0 | 78.0 |
| Conventional_Fuel_Economy_City | NaN | NaN |
| Conventional_Fuel_Economy_Highway | NaN | NaN |
| Conventional_Fuel_Economy_Combined | NaN | NaN |
| Transmission_Type | Auto | Auto |
| Engine_Type | e-motor | e-motor |
| Engine_Size | 175 kW electric motor | 141 kW and 172 kW electric motor |
| Engine_Cylinder_Count | NaN | NaN |
| Engine_Description | 175 kW electric motor;\r\n129 Ah battery | 141 kW and 172 kW electric motors;\r\n240 Ah b... |
| Manufacturer | Audi | Audi |
| Category | Sedan/Wagon | SUV |
| Fuel | Electric | Electric |
| Electric-Only_Range | 238.0 | 222.0 |
| PHEV_Total_Range | NaN | NaN |
| PHEV_Type | NaN | NaN |
| Drivetrain | AWD | AWD |
| combined_fuel_economy | NaN | NaN |
| Name | Audi e-tron GT | Audi e-tron quattro |
import os
import random
import plotly.express as px
import plotly.graph_objects as go
from plotly.subplots import make_subplots
import csv
fig = px.bar(df_fe_aer[:30], x='Name', y='Electric-Only_Range', title="Electric Car Ranking", height=700)
fig.show()
df_ph=df[df['Fuel']=='Plug-in Hybrid Electric'].reset_index(drop=True)
display(df_ph[0:2].T)
df_ph_aer=df_ph[['Name', 'PHEV_Total_Range']]
df_ph_aer=df_ph_aer.dropna().drop_duplicates()
df_ph_aer=df_ph_aer.groupby('Name',as_index=False).max()
df_ph_aer=df_ph_aer.sort_values('PHEV_Total_Range',ascending=False)
fig2 = px.bar(df_ph_aer[:30], x='Name', y='PHEV_Total_Range', title="Plug In Hybrid Range", height=700)
fig2.show()
| 0 | 1 | |
|---|---|---|
| Unnamed: 0 | 14 | 28 |
| Vehicle_ID | 12831.0 | 12832.0 |
| Model | A7 TFSI e quattro | Q5 TFSI e quattro |
| Model_Year | 2022.0 | 2022.0 |
| Alternative_Fuel_Economy_City | NaN | NaN |
| Alternative_Fuel_Economy_Highway | NaN | NaN |
| Alternative_Fuel_Economy_Combined | 70.0 | 61.0 |
| Conventional_Fuel_Economy_City | 25.0 | 25.0 |
| Conventional_Fuel_Economy_Highway | 31.0 | 27.0 |
| Conventional_Fuel_Economy_Combined | 27.0 | 26.0 |
| Transmission_Type | Auto | Auto |
| Engine_Type | SI | SI |
| Engine_Size | 2.0L | 2.0L |
| Engine_Cylinder_Count | 4.0 | 4.0 |
| Engine_Description | 2.0L I4;\r\n105 kW electric motor | 2.0L I4;\r\n105 kW electric motor |
| Manufacturer | Audi | Audi |
| Category | Sedan/Wagon | Sedan/Wagon |
| Fuel | Plug-in Hybrid Electric | Plug-in Hybrid Electric |
| Electric-Only_Range | 26.0 | 23.0 |
| PHEV_Total_Range | 410.0 | 390.0 |
| PHEV_Type | Blended | Blended |
| Drivetrain | AWD | AWD |
| combined_fuel_economy | 97.0 | 87.0 |
| Name | Audi A7 TFSI e quattro | Audi Q5 TFSI e quattro |
df[(df['Engine_Cylinder_Count'] == 6) & (df['Category'] == "SUV")]
| Unnamed: 0 | Vehicle_ID | Model | Model_Year | Alternative_Fuel_Economy_City | Alternative_Fuel_Economy_Highway | Alternative_Fuel_Economy_Combined | Conventional_Fuel_Economy_City | Conventional_Fuel_Economy_Highway | Conventional_Fuel_Economy_Combined | ... | Engine_Description | Manufacturer | Category | Fuel | Electric-Only_Range | PHEV_Total_Range | PHEV_Type | Drivetrain | combined_fuel_economy | Name | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 29 | 29 | 12797.0 | Q7 quattro | 2022.0 | NaN | NaN | NaN | 18.0 | 23.0 | 20.0 | ... | 3.0L V6 | Audi | SUV | Hybrid Electric | NaN | NaN | NaN | AWD | NaN | Audi Q7 quattro |
| 30 | 30 | 12798.0 | Q8 quattro | 2022.0 | NaN | NaN | NaN | 18.0 | 23.0 | 20.0 | ... | 3.0L V6 | Audi | SUV | Hybrid Electric | NaN | NaN | NaN | AWD | NaN | Audi Q8 quattro |
| 60 | 60 | 12860.0 | X3 M40i | 2022.0 | NaN | NaN | NaN | 21.0 | 26.0 | 23.0 | ... | 3.0L V6 | BMW | SUV | Hybrid Electric | NaN | NaN | NaN | AWD | NaN | BMW X3 M40i |
| 61 | 61 | 12861.0 | X4 M40i | 2022.0 | NaN | NaN | NaN | 21.0 | 26.0 | 23.0 | ... | 3.0L V6 | BMW | SUV | Hybrid Electric | NaN | NaN | NaN | AWD | NaN | BMW X4 M40i |
| 62 | 62 | 12862.0 | X5 sDrive40i | 2022.0 | NaN | NaN | NaN | 21.0 | 26.0 | 23.0 | ... | 3.0L V6 | BMW | SUV | Hybrid Electric | NaN | NaN | NaN | RWD | NaN | BMW X5 sDrive40i |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 2741 | 2741 | 556.0 | Mountaineer Wagon FFV | 2003.0 | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | Mercury | SUV | Ethanol (E85) | NaN | NaN | NaN | NaN | NaN | Mercury Mountaineer Wagon FFV |
| 2766 | 2766 | 179.0 | Explorer FFV | 2002.0 | 11.0 | 15.0 | NaN | 15.0 | 20.0 | NaN | ... | NaN | Ford | SUV | Ethanol (E85) | NaN | NaN | NaN | NaN | NaN | Ford Explorer FFV |
| 2767 | 2767 | 180.0 | Explorer Sport FFV | 2002.0 | 18.0 | 22.0 | NaN | NaN | NaN | NaN | ... | NaN | Ford | SUV | Ethanol (E85) | NaN | NaN | NaN | NaN | NaN | Ford Explorer Sport FFV |
| 2768 | 2768 | 181.0 | Explorer Sport Track FFV | 2002.0 | 16.0 | 20.0 | NaN | NaN | NaN | NaN | ... | NaN | Ford | SUV | Ethanol (E85) | NaN | NaN | NaN | NaN | NaN | Ford Explorer Sport Track FFV |
| 2813 | 2813 | 138.0 | Explorer Sport FFV | 2001.0 | 12.0 | 16.0 | NaN | 16.0 | 21.0 | NaN | ... | NaN | Ford | SUV | Ethanol (E85) | NaN | NaN | NaN | NaN | NaN | Ford Explorer Sport FFV |
259 rows × 24 columns
df[(df['Engine_Cylinder_Count'] == 4) & (df['Category'] == "SUV")]
| Unnamed: 0 | Vehicle_ID | Model | Model_Year | Alternative_Fuel_Economy_City | Alternative_Fuel_Economy_Highway | Alternative_Fuel_Economy_Combined | Conventional_Fuel_Economy_City | Conventional_Fuel_Economy_Highway | Conventional_Fuel_Economy_Combined | ... | Engine_Description | Manufacturer | Category | Fuel | Electric-Only_Range | PHEV_Total_Range | PHEV_Type | Drivetrain | combined_fuel_economy | Name | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 25 | 25 | 13023.0 | Q5 quattro | 2022.0 | NaN | NaN | NaN | 23.0 | 28.0 | 25.0 | ... | 2.0L I4 | Audi | SUV | Hybrid Electric | NaN | NaN | NaN | AWD | NaN | Audi Q5 quattro |
| 26 | 26 | 12795.0 | Q5 S line quattro | 2022.0 | NaN | NaN | NaN | 23.0 | 28.0 | 25.0 | ... | 2.0L I4 | Audi | SUV | Hybrid Electric | NaN | NaN | NaN | AWD | NaN | Audi Q5 S line quattro |
| 27 | 27 | 12796.0 | Q5 Sportback S line quattro | 2022.0 | NaN | NaN | NaN | 23.0 | 28.0 | 25.0 | ... | 2.0L I4 | Audi | SUV | Hybrid Electric | NaN | NaN | NaN | AWD | NaN | Audi Q5 Sportback S line quattro |
| 90 | 90 | 13037.0 | Escape FWD PHEV | 2022.0 | NaN | NaN | 105.0 | 43.0 | 38.0 | 41.0 | ... | 2.5L I4;\r\n96 kW electric motor | Ford | SUV | Plug-in Hybrid Electric | 37.0 | 520.0 | Blended | FWD | 146.0 | Ford Escape FWD PHEV |
| 134 | 134 | 12886.0 | CR-V AWD | 2022.0 | NaN | NaN | NaN | 40.0 | 35.0 | 38.0 | ... | 2.0L I4 | Honda | SUV | Hybrid Electric | NaN | NaN | NaN | AWD | NaN | Honda CR-V AWD |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 2578 | 2578 | 680.0 | Mariner | 2007.0 | 33.0 | 29.0 | NaN | NaN | NaN | NaN | ... | NaN | Mercury | SUV | Hybrid Electric | NaN | NaN | NaN | NaN | NaN | Mercury Mariner |
| 2582 | 2582 | 697.0 | VUE Green Line | 2007.0 | 27.0 | 32.0 | NaN | NaN | NaN | NaN | ... | NaN | Saturn | SUV | Hybrid Electric | NaN | NaN | NaN | NaN | NaN | Saturn VUE Green Line |
| 2604 | 2604 | 489.0 | Escape Hybrid | 2006.0 | 36.0 | 31.0 | NaN | NaN | NaN | NaN | ... | NaN | Ford | SUV | Hybrid Electric | NaN | NaN | NaN | NaN | NaN | Ford Escape Hybrid |
| 2619 | 2619 | 492.0 | Mariner Hybrid | 2006.0 | 33.0 | 29.0 | NaN | NaN | NaN | NaN | ... | NaN | Mercury | SUV | Hybrid Electric | NaN | NaN | NaN | NaN | NaN | Mercury Mariner Hybrid |
| 2636 | 2636 | 375.0 | Escape Hybrid | 2005.0 | 36.0 | 31.0 | NaN | NaN | NaN | NaN | ... | NaN | Ford | SUV | Hybrid Electric | NaN | NaN | NaN | NaN | NaN | Ford Escape Hybrid |
180 rows × 24 columns
new_df = pd.DataFrame(df)
df.update(new_df)
new_df
| Unnamed: 0 | Vehicle_ID | Model | Model_Year | Alternative_Fuel_Economy_City | Alternative_Fuel_Economy_Highway | Alternative_Fuel_Economy_Combined | Conventional_Fuel_Economy_City | Conventional_Fuel_Economy_Highway | Conventional_Fuel_Economy_Combined | ... | Engine_Description | Manufacturer | Category | Fuel | Electric-Only_Range | PHEV_Total_Range | PHEV_Type | Drivetrain | combined_fuel_economy | Name | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | 13044.0 | NSX | 2022.0 | NaN | NaN | NaN | 21.0 | 22.0 | 21.0 | ... | 3.5L V6 | Acura | Sedan/Wagon | Hybrid Electric | NaN | NaN | NaN | AWD | NaN | Acura NSX |
| 1 | 1 | 12854.0 | A3 | 2022.0 | NaN | NaN | NaN | 29.0 | 38.0 | 32.0 | ... | 2.0L I4 | Audi | Sedan/Wagon | Hybrid Electric | NaN | NaN | NaN | FWD | NaN | Audi A3 |
| 2 | 2 | 12842.0 | A3 quattro | 2022.0 | NaN | NaN | NaN | 28.0 | 36.0 | 31.0 | ... | 2.0L I4 | Audi | Sedan/Wagon | Hybrid Electric | NaN | NaN | NaN | AWD | NaN | Audi A3 quattro |
| 3 | 3 | 12783.0 | A4 allroad quattro | 2022.0 | NaN | NaN | NaN | 24.0 | 30.0 | 26.0 | ... | 2.0L I4 | Audi | Sedan/Wagon | Hybrid Electric | NaN | NaN | NaN | AWD | NaN | Audi A4 allroad quattro |
| 4 | 4 | 12782.0 | A4 quattro | 2022.0 | NaN | NaN | NaN | 26.0 | 34.0 | 29.0 | ... | 2.0L I4 | Audi | Sedan/Wagon | Hybrid Electric | NaN | NaN | NaN | AWD | NaN | Audi A4 quattro |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 1048570 | 1048570 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 1048571 | 1048571 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 1048572 | 1048572 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 1048573 | 1048573 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 1048574 | 1048574 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 66.0 | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
1048575 rows × 24 columns
df.to_csv('Lightdutyvehicles_final.txt')